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The global carbon sequestration and avoided emissions potentially achieved via blue carbon is high (∼3% of annual global greenhouse gas emissions); however, it is limited by multidisciplinary and interacting uncertainties spanning the social, governance, financial, and technological dimensions. We compiled a transdisciplinary team of experts to elucidate these challenges and identify a way forward. Key actions to enhance blue carbon as a natural climate solution include improving policy and legal arrangements to ensure equitable sharing of benefits; improving stewardship by incorporating indigenous knowledge and values; clarifying property rights; improving financial approaches and accounting tools to incorporate co-benefits; developing technological solutions for measuring blue carbon sequestration at low cost; and resolving knowledge gaps regarding blue carbon cycles. Implementing these actions and operationalizing blue carbon will achieve measurable changes to atmospheric greenhouse gas concentrations, provide multiple co-benefits, and address national obligations associated with international agreements.

This work introduces a comprehensive approach to assess the sensitivity of model outputs to changes in parameter values, constrained by the combination of prior beliefs and data. This approach identifies stiff parameter combinations strongly affecting the quality of the model-data fit while simultaneously revealing which of these key parameter combinations are informed primarily by the data or are also substantively influenced by the priors. We focus on the very common context in complex systems where the amount and quality of data are low compared to the number of model parameters to be collectively estimated, and showcase the benefits of this technique for applications in biochemistry, ecology, and cardiac electrophysiology. We also show how stiff parameter combinations, once identified, uncover controlling mechanisms underlying the system being modeled and inform which of the model parameters need to be prioritized in future experiments for improved parameter inference from collective model-data fitting.

It can be difficult to identify ways to reduce the complexity of large models whilst maintaining predictive power, particularly where there are hidden parameter interdependencies. Here, we demonstrate that the analysis of model sloppiness can be a new invaluable tool for strategically simplifying complex models. Such an analysis identifies parameter combinations which strongly and/or weakly inform model behaviours, yet the approach has not previously been used to inform model reduction. Using a case study on a coral calcification model calibrated to experimental data, we show how the analysis of model sloppiness can strategically inform model simplifications which maintain predictive power. Additionally, when comparing various approaches to analysing sloppiness, we find that Bayesian methods can be advantageous when unambiguous identification of the best-fit model parameters is a challenge for standard optimisation procedures.

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution. In this paper, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution. For computations in low-dimensional distributions, the semi-analytical method is fast and thus should be considered. As the dimension increases, the Gessner et al. method becomes the only practically efficient approach of the methods tested here.

Recycled concrete aggregate (RCA) has been used as an alternative sustainable material in the construction industry, but RCA long-term environmental impacts are unknown. In this study, the bacterial enrichment potential to reduce the alkalinity of two different types of RCA was examined, from laboratory-produced concrete and from a stockpile of demolished concrete that had been in service in transportation applications. Washed and un-washed lab and field RCA were biostimulated by being exposed to ATCC® Medium 661 in batch experiments. pH, metal composition and microbial community changes in the leachates were monitored over time. Results show that initial pH of field RCA leachate could be decreased to less concerning values, as low as 8, but concentrations of some metals in the leachate exceeded groundwater quality standards. However, the biostimulated RCA released lower metal concentration and was more resistant to pH increases than non-biostimulated RCA during a long-term leaching experiment with DI water. The microbial community was enriched on anaerobic, halotolerant and alkaliphile microorganisms, resistant to extreme environmental conditions. The outcome of this research suggests a baseline for field RCA pretreatment before field application, using a biostimulation method that would generate a less environmentally detrimental runoff.

Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize ecosystem interactions or reliably calibrate or validate such models. Here we demonstrate a different approach, using a minimum realistic model to guide decisions in data- and resource-scarce systems. We illustrate our approach with a case-study in an invaded ecosystem from Christmas Island, Australia, where there are concerns that cat eradication to protect native species, including the red-tailed tropicbird, could release meso-predation by invasive rats. We use biophysical constraints (metabolic demand) and observable parameters (e.g. prey preferences) to assess the combined cat and rat abundances which would threaten the tropicbird population. We find that the population of tropicbirds cannot be sustained if predated by 1607 rats (95% credible interval (CI) [103, 5910]) in the absence of cats, or 21 cats (95% CI [2, 82]) in the absence of rats. For every cat removed from the island, the bird's net population growth rate improves, provided that the rats do not increase by more than 77 individuals (95% CI [30, 174]). Thus, in this context, one cat is equivalent to 30-174 rats. Our methods are especially useful for on-the-ground predator control in the absence of knowledge of predator-predator interactions, to assess whether 1) the current abundance of predators threatens the prey population of interest, 2) managing one predator species alone is sufficient to protect the prey species given potential release of another predator, and 3) control of multiple predator species is needed to meet the conservation goal. Our approach demonstrates how to use limited information for maximum value in data-poor systems, by shifting the focus from predicting future trajectories, to identifying conditions which threaten the conservation goal. This article is protected by copyright. All rights reserved.

Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confidence and improve their potential as a biomarker to monitor the progression of Parkinson’s disease (PD). In the present work, we aim to predict motor outcome from baseline DAT SPECT imaging radiomic features and clinical measures using machine learning techniques. We designed and trained artificial neural networks (ANNs) to analyze the data from 69 patients within the Parkinson’s Progressive Marker Initiative (PPMI) database. The task was to predict the unified PD rating scale (UPDRS) part III motor score in year 4 from 92 imaging features extracted on 12 different regions as well as 6 non-imaging measures at baseline (year 0). We first performed univariate screening (including the adjustment for false discovery) to select 4 regions each having 10 features with significant performance in classifying year 4 motor outcome into two classes of patients (divided by the UPDRS III threshold of 30). The leave-one-out strategy was then applied to train and test the ANNs for individual and combinations of features. The prediction statistics were calculated from 100 rounds of experiments, and the accuracy in appropriate prediction (classification of year 4 outcome) was quantified. Out of the baseline non-imaging features, only the UPDRS III (at year 0) was predictive, while multiple imaging features depicted significance. The different selected features reached a predictive accuracy of 70 % if used individually. Combining the top imaging features from the selected regions significantly improved the prediction accuracy to 75 % (p

Saltmarsh is a coastal ecosystem providing crucial ecosystem services, and its continued degradation and fragmentation has drawn increasing attention. However, how to effectively restore the connectivity between fragmented saltmarsh patches remains an open challenge. In this study, we developed a metric and modelling framework that prioritised saltmarsh patches for restoration. To demonstrate our approach, we simulated spatially explicit restoration schedules for Suaeda salsa patches at the Yellow River Delta National Nature Reserve, China, using three strategies: increasing-patch-area, increasing-number-of-patches and a benchmark unrestrictive prioritization strategy. We prioritised patches for restoration based on a number of widely used graph-theoretic landscape connectivity and metapopulation capacity metrics. Our simulation results suggested the rank connectivity-importance of extant patches was correlated within the group of graph-theoretic connectivity metrics or metapopulation capacity metrics, but unrelated across group. The unrestrictive prioritization strategy clearly outperformed the strategies of increasing-patch-area and increasing-number-of-patches which returned comparable connectivity restoration outcomes. For the more effective unrestrictive prioritization strategy, there were substantial differences in the simulated priority patches between metrics that considered stepping stone effects and those did not. While the former resulted in corridor-building priority patches that led to a more connected landscape throughout the region, the latter led to local clustering. We recommend use of the total probability of connectivity (PC) among the metrics we tested due to similarity of results to other metrics and its simulation efficiency. The proposed framework is readily applicable to prioritise areas for connectivity conservation and restoration in any monospecific ecosystem at the regional scale.

Catchment impacts on downstream ecosystems are difficult to quantify, but important for setting management targets. Here we compared 12 years of monitoring data of seagrass area and biomass in Cleveland Bay, northeast Australia, with discharge and associated sediment loads from nearby rivers. Seagrass biomass and area exhibited different trajectories in response to river inputs. River discharge was a slightly better predictor of seagrass indicators than total suspended solid (TSS) loads, indicating that catchment effects on seagrass are not restricted to sediment. Linear relationships between Burdekin River TSS loads delivered over 1–4 years and seagrass condition in Cleveland Bay generated Ecologically Relevant Targets (ERT) for catchment sediment inputs. Our predicted ERTs were comparable to those previously estimated using mechanistic models. This study highlights the challenges of linking catchment inputs to condition of downstream ecosystems, and the importance of integrating a variety of metrics and approaches to increase confidence in ERTs.

Recycled concrete aggregate (RCA) is a key tool for improving sustainability in the construction and demolition industries. The use of RCA in new concrete is, in part, obstructed by the dea...

A computational modeling procedure for generating a random aggregate structure (RAS) for recycled concrete aggregate (RCA) materials is proposed in this paper. The two-dimensional aggregate...

Seagrass ecosystems are inherently dynamic, responding to environmental change across a range of scales. Habitat requirements of seagrass are well defined, but less is known about their ability to resist disturbance. Specific means of recovery after loss are particularly difficult to quantify. Here we assess the resistance and recovery capacity of 12 seagrass genera. We document four classic trajectories of degradation and recovery for seagrass ecosystems, illustrated with examples from around the world. Recovery can be rapid once conditions improve, but seagrass absence at landscape scales may persist for many decades, perpetuated by feedbacks and/or lack of seed or plant propagules to initiate recovery. It can be difficult to distinguish between slow recovery, recalcitrant degradation, and the need for a window of opportunity to trigger recovery. We propose a framework synthesizing how the spatial and temporal scales of both disturbance and seagrass response affect ecosystem trajectory and hence resilience.

Trace element accumulation is an anthropogenic threat to seagrass ecosystems, which in turn may affect the health of humans who depend on these ecosystems. Trace element accumulation in seagrass meadows may vary temporally due to, e.g., seasonal patterns in sediment discharge from upstream areas. In addition, when several trace elements are present in sufficiently high concentrations, the risk of seagrass loss due to the cumulative impact of these trace elements is increased. To assess the seasonal variation and cumulative risk of trace element contamination to seagrass meadows, trace element (As, Cd, Cr, Cu, Pb, Hg, Mn and Zn) levels in surface sediment and seagrass tissues were measured in the largest Chinese Zostera japonica habitat, located in the Yellow River Estuary, at three sites and three seasons (fall, spring and summer) in 2014-2015. In all three seasons, trace element accumulation in the sediment exceeded background levels for Cd and Hg. Cumulative risk to Z. japonica habitat in the Yellow River Estuary, from all trace elements together, was assessed as "moderate" in all three seasons examined. Bioaccumulation of trace elements by seagrass tissues was highly variable between seasons and between above-ground and below-ground biomass. The variation in trace element concentration of seagrass tissues was much higher than the variation in trace element concentration of the sediment. In addition, for trace elements which tended to accumulate more in above-ground biomass than below-ground biomass (Cd and Mn), the ratio of above-ground to below-ground trace element concentration peaked at times corresponding to high water discharge and high sediment loads in the Yellow River Estuary. Overall, our results suggest that trace element accumulation in the sediment may not vary between seasons, but bioaccumulation in seagrass tissues is highly variable and may respond directly to trace elements in the water column.

This paper investigates the effects of adhered mortar content on the mechanical properties of recycled aggregate concrete (RAC) systems using two-dimensional finite element analysis of RAC specimens subjected to uniaxial compression. Sensitivity and statistical analyses of RAC systems were conducted to explore how individual material stiffnesses (aggregate, mortar matrix, adhered mortar, new Interfacial Transition Zone (ITZ), and old ITZ) and adhered mortar contents (2, 10, 20 and 50%) influence RAC mechanical performance. In total, 128 simulation results were performed to understand variability in stress development, damage progression, compressive strength, and elastic modulus of RAC systems. Statistical inferences on the effects of variability in material stiffness and adhered mortar content were made based on frequency distributions, probability density functions, Pareto charts, main effects plots, and bivariate contour plots. Numerical results showed that compressive strength and elastic modulus decreased with increasing adhered mortar contents, while the strain corresponding to compressive softening increased with adhered mortar contents. Statistical results showed that compressive strength was most significantly influenced by aggregate stiffness and mortar matrix stiffness. Strain localizations were observed near the aggregate boundaries due to large material stiffness discontinuities in the RAC meso-level structure. RAC elastic modulus and ultimate compressive strain were mainly governed by the stiffness of the mortar matrix. Based on the numerical results, bivariate contour plots were developed to understand how variations in material stiffness and adhered mortar content influence the strength and stiffness of RAC systems.

Seagrasses are globally important coastal habitat‐forming species, yet it is unknown how seagrasses respond to the combined pressures of ocean acidification and warming of sea surface temperature. We exposed three tropical species of seagrass (Cymodocea serrulata, Halodule uninervis, and Zostera muelleri) to increasing temperature (21, 25, 30, and 35°C) and pCO2 (401, 1014, and 1949 μatm) for 7 wk in mesocosms using a controlled factorial design. Shoot density and leaf extension rates were recorded, and plant productivity and respiration were measured at increasing light levels (photosynthesis–irradiance curves) using oxygen optodes. Shoot density, growth, photosynthetic rates, and plant‐scale net productivity occurred at 25°C or 30°C under saturating light levels. High pCO2 enhanced maximum net productivity for Z. muelleri, but not in other species. Z. muelleri was the most thermally tolerant as it maintained positive net production to 35°C, yet for the other species there was a sharp decline in productivity, growth, and shoot density at 35°C, which was exacerbated by pCO2. These results suggest that thermal stress will not be offset by ocean acidification during future extreme heat events and challenges the current hypothesis that tropical seagrass will be a ‘winner’ under future climate change conditions.

Submerged macrophytes can colonize shallow lakes via several reproductive mechanisms, and can in turn substantially alter these environments by modifying the thermal structure and dissolved oxygen levels within these lakes. Although multiple mechanisms of submerged macrophyte expansion have been described, the relative contribution of each of these in shallow lake environments has been largely overlooked. In this study we analyzed the spatial spread and patterning during seasonal growth of a globally invasive submerged macrophyte, Potamogeton crispus, in a shallow urban lake (Lake Monger, Western Australia). We used underwater and aerial imagery to estimate the spatial pattern of the P. crispus bed. By comparing the spatial extent of the bed at different times during the growing season, we found linear expansion rates two orders of magnitude higher than those previously estimated through rhizome elongation. We formulated a deterministic mathematical model that accounted for the ability of P. crispus to spread through rhizomes and fragments broken off by the feeding activities of aquatic birds, to assess the contribution of fragment dispersal to the emergent patterns of the submerged macrophyte bed. In addition to accounting for dispersal from fragments, the model also accounted for a hypothesized feedback between macrophyte-induced thermal stratification and central dieback. Comparison of our model results against field data indicated that the model accurately represented the spatial spread of the macrophyte bed when fragment dispersal was included. When fragment dispersal was not included in the model, the spatial spread of the bed was largely underestimated, suggesting that fragment dispersal may well account for the fast seasonal spread of this species. The model also captured the formation of a ring-shaped pattern in spatial macrophyte distribution suggesting that both fragment dispersal and the feedback between stratification and dieback are necessary to reproduce the spatial structure of the macrophyte bed. Our results highlight the potential important role of fragment dispersal in facilitating colonization and submerged macrophyte invasion in shallow lakes.

Calcium aluminate cement (CAC) is gaining popularity in North America as a rapid repair material due to its ability to quickly gain strength, even at low curing temperatures. Use of CAC has been limited due to a lack of understanding of the process of conversion and the role of aggregates in CAC concrete. Conversion, which occurs only in 100% CAC systems, is a chemical process in which metastable hydrates convert into denser, stable hydrates. Presented is an examination of aggregate source impacts on this conversion process and converted CAC concrete strengths. Nine different concrete systems with fifteen varying aggregate sources were examined. Results indicated that carbonate limestone and siliceous limestone aggregate systems had significantly less strength reduction due to conversion compared to siliceous aggregate systems. Microstructural analysis of systems suggested that the carbonate limestone system had less porosity and better-formed aggregate/paste interfacial transition zones compared to the siliceous systems. Chemical analysis of the concrete pore solution indicated that the carbonate limestone system's pH and ionic concentrations of aluminum, sodium, and potassium were significantly higher than that of a siliceous system, indicating more dissolution of unhydrated cement in the carbonate limestone system. These studies are presented along with a proposed theory explaining the cause of the significant converted strength differences in CAC concrete systems made with limestone aggregates compared to siliceous aggregates.

Catchment degradation causing increased sediment flow is one of the key stressors facing Great Barrier Reef (GBR) habitats. Ecologically relevant targets (ERTs) for sediment and nutrient loads have been previously proposed based on seagrass light requirements, the next step is to connect these to ecological response. The overarching goal of the present work is to recommend preliminary thresholds that can be used in the development of more refined ERTs. To achieve this, we perform statistical analysis on datasets for catchment flows and sediment loads and condition of the adjacent seagrass habitat, to identify what might be the direct impacts of catchment discharge on seagrass and the associated timescales of ecological response.Our case study focuses on Cleveland Bay, which is located in the central GBR, and has important seagrass habitat that is affected by discharge from the Burdekin River. Annual monitoring of seagrass biomass and area has been undertaken since 2007. We compare these ecological time-series with data for Burdekin River annual flow and total sediment load from 2005 onwards. Annual Burdekin River flow varied by nearly 40-fold within the 2005-2018 study period, and declines in biomass and area of both subtidal and intertidal seagrasses were associated with high flows and loads from the Burdekin. Subtidal seagrasses appeared more sensitive to changes in catchment discharges than intertidal seagrasses, exhibiting a 3 year timeframe for recovery, following high annual flows and loads. Based on our results, a linear model relating change in seagrass biomass to Burdekin River metrics was used to calculate predicted thresholds below which seagrass biomass was likely to increase, and above which biomass was likely to decline. For seagrass area, a growth threshold, below which seagrass area expanded; and a decline threshold, above which seagrass area fell, were defined for annual Burdekin River flow, and sediment load. Overall these thresholds provide the first steps towards refining ERTs based on ecological condition, which can directly inform the management of the GBR to protect its iconic seagrass habitats and associated communities. The next step is to examine whether the relationship between river discharge and sediment load was the primary cause of seagrass decline.

This Research Topic presents new research investigating the coupling between physical (fluid dynamics, mass transport, and light availability) and biological (nutrient cycling, particle transport, ecosystem structure, and biodiversity) processes in aquatic canopies. The starting point for this topic was the observation that our notion of “canopy” in the aquatic sciences, in contrast to that of our terrestrially-focused colleagues, remains underdeveloped. Forest canopy studies have been considered a new field of science (Nadkarni et al., 2011) and the concept of forest canopy research is clearly documented in the literature (Barker and Pinard, 2001; Nadkarni, 2001; Lowman, 2009); we have not found similar mentions of the canopy concept in aquatic studies. Over the past decade, however, there has been an increase in the number of studies on underwater canopies, as well as a shift toward more multidisciplinary studies that consider more than just the physical impacts of the canopy's presence (Ackerman, 2007; Nepf et al., 2007; O'Brien et al., 2014)...

Since the mid-1990s, Australia's Commonwealth Science Industry and Research Organisation (CSIRO) has been developing a biogeochemical (BGC) model for coupling with a hydrodynamic and sediment model for application in estuaries, coastal waters and shelf seas. The suite of coupled models is referred to as the CSIRO Environmental Modelling Suite (EMS) and has been applied at tens of locations around the Australian continent. At a mature point in the BGC model's development, this paper presents a full mathematical description, as well as links to the freely available code and user guide. The mathematical description is structured into processes so that the details of new parameterisations can be easily identified, along with their derivation. In EMS, the underwater light field is simulated by a spectrally resolved optical model that calculates vertical light attenuation from the scattering and absorption of 20+ optically active constituents. The BGC model itself cycles carbon, nitrogen, phosphorous and oxygen through multiple phytoplankton, zooplankton, detritus and dissolved organic and inorganic forms in multiple water column and sediment layers. The water column is dynamically coupled to the sediment to resolve deposition, resuspension and benthic–pelagic biogeochemical fluxes. With a focus on shallow waters, the model also includes detailed representations of benthic plants such as seagrass, macroalgae and coral polyps. A second focus has been on, where possible, the use of geometric derivations of physical limits to constrain ecological rates. This geometric approach generally requires population-based rates to be derived from initially considering the size and shape of individuals. For example, zooplankton grazing considers encounter rates of one predator on a prey field based on summing relative motion of the predator with the prey individuals and the search area; chlorophyll synthesis includes a geometrically derived self-shading term; and the bottom coverage of benthic plants is calculated from their biomass using an exponential form derived from geometric arguments. This geometric approach has led to a more algebraically complicated set of equations when compared to empirical biogeochemical model formulations based on populations. But while being algebraically complicated, the model has fewer unconstrained parameters and is therefore simpler to move between applications than it would otherwise be. The version of EMS described here is implemented in the eReefs project that delivers a near-real-time coupled hydrodynamic, sediment and biogeochemical simulation of the Great Barrier Reef, northeast Australia, and its formulation provides an example of the application of geometric reasoning in the formulation of aquatic ecological processes.

The concrete design and construction industry has been eagerly adopting sustainability initiatives over the last 20 years to help reduce its carbon footprint, improve economic sustainability, and ensure the important natural resources that support the industry remain available for years to come. Despite these advances, recycled concrete aggregates (RCA) have been significantly underutilized even though they are becoming an increasingly available sustainable resource that can be used to reduce the amount of natural aggregates used in concrete. Use of RCA has been limited, in part, because of a lack of standardized guidance on creating mixture designs and how aggregates may impact concrete properties. Presented in this paper are the results of a statistical analysis of data from over 80 peer reviewed articles that can be used to predict performance in mixtures containing RCA. A large database consisting of 850 compressive strength, 682 elastic modulus, 197 flexural strength, and 462 splitting tensile strength results was created from this literature review with data ranging from 1988 to 2018. Non-linear regression analyses determined that the empirical trends related to compressive strength, elastic modulus, flexural strength, and splitting tensile strength had a linear relationship with the RCA replacement levels depending on absorption capacity, RCA replacement level, effective water-to-cement ratio, maximum aggregate size, and strength of the RCA’s parent concrete. Overall, the RCA replacement ratio and the water-to-cement ratio had significant impacts on concrete mechanical strengths, while other factors such as mix proportioning parameters and aggregate properties had minor effects on the mechanical properties of RCA systems over the large dataset. Additionally, significant reductions in strength were not observed until replacement levels of RCA increased above 20%.

Well-intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem-wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision-makers to select interventions. Using these time-series data (sparse and noisy datasets drawn from deterministic Lotka-Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species' future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well-constrained predictions before they can inform decisions that improve environmental outcomes.

Tree stems swell and shrink daily, which is thought to reflect changes in the volume of water within stem tissues. We observed these daily patterns using automatic dendrometer bands in a diverse group of mangrove species over five mangrove forests across Australia and New Caledonia. We found that mangrove stems swelled during the day and shrank at night. Maximum swelling was highly correlated with daily maxima in air temperature. Variation in soil salinity and levels of tidal inundation did not influence the timing of stem swelling over all species. Medium-term increases in stem circumference were highly sensitive to rainfall. We defoliated trees to assess the role of foliar transpiration in stem swelling and shrinking. Defoliated trees showed maintenance of the pattern of daytime swelling, indicating that processes other than canopy transpiration influence the temporary stem diameter increments, which could include thermal swelling of stems. More research is required to understand the processes contributing to stem shrinking and swelling. Automatic Dendrometer Bands could provide a useful tool for monitoring the response of mangroves to extreme climatic events as they provide high-frequency, long-term, and large-scale information on tree water status.

The use of recycled concrete aggregates (RCA) as a replacement in new concrete has gained popularity worldwide as a method of reducing natural aggregate consumption. Many engineers, owners, and agencies are reticent to use RCA due to a lack of understanding on how the material will impact overall concrete properties. This lack of acceptance has been hindered by the difficulty of doing large-scale studies because of the time and cost of casting different concrete mixtures. Many individual studies have been completed but little work has been done to analyze that work to make broad conclusions on RCA concrete mechanical properties. This paper presents the development and analysis of a database of mechanical properties of concrete containing coarse RCA generated from 115 peer-reviewed journal articles. A sensitivity study of RCA systems was conducted through a full-factorial analysis to explore how the mixture design proportions influence the RCA concrete hardened properties. On average, the compressive strengths of concrete systems with 20% or less RCA replacement were 2% lower than that of the natural aggregate systems. Increased levels of RCA replacements decreased the average concrete compressive strengths with the highest average strength loss, in which a 10% reduction compared to natural aggregate systems is observed at 100% RCA replacement level. Results indicate that, in general, adequate concrete strengths for a variety of applications can be achieved at all replacement ratios of coarse RCA. Impacts of mixture design properties on particular mechanical properties are presented along with bivariate contour plots that can be used by concrete mixture designers to better estimate RCA concrete properties. This work supports an increased adoption of RCA by providing broad conclusions supported by statistical analysis that show typical performance of RCA concrete across a variety of design inputs.

A feedback between seagrass presence, suspended sediment and benthic light can induce bistability between two ecosystem states: one where the presence of seagrass reduces suspended sediment concentrations to increase benthic light availability thereby favoring growth, and another where seagrass absence increases turbidity thereby reducing growth. This literature review identifies (1) how the environmental and seagrass meadow characteristics influence the strength and direction (stabilizing or destabilizing) of the seagrass-sediment-light feedback, and (2) how this feedback has been incorporated in ecosystem models proposed to support environmental decision making. Large, dense seagrass meadows in shallow subtidal, non-eutrophic systems, growing in sediments of mixed grain size and subject to higher velocity flows, have the greatest potential to generate bistability via the seagrass-sediment-light feedback. Conversely, seagrass meadows of low density, area and height can enhance turbulent flows that interact with the seabed, causing water clarity to decline. Using a published field experiment as a case study, we show that the seagrass-sediment-light feedback can induce bistability only if the suspended sediment has sufficient light attenuation properties. The seagrass-sediment-light feedback has been considered in very few ecosystem models. These models have the potential to identify areas where bistability occurs, which is information that can assist in spatial prioritization of conservation and restoration efforts. In areas where seagrass is present and bistability is predicted, recovery may be difficult once this seagrass is lost. Conversely, bare areas where seagrass presence is predicted (without bistability) may be better targets for seagrass restoration than bare areas where bistability is predicted.

This study tested the hypothesis that concrete produced with recycled concrete aggregate (RCA) has increased cracking resistance compared to concrete incorporating natural aggregates. Two sources of RCA were used: (1) a laboratory created RCA produced by crushing previously produced laboratory concrete; and (2) a field RCA obtained from a demolished airfield pavement. Mechanical properties, and shrinkage and cracking due to drying of concrete produced with RCA replacement were studied. Adequate compressive strength (39.3–43.4 MPa), splitting tensile strength (3.7–4.4 MPa), and modulus of elasticity (27.2–28.3 GPa) were obtained in mixtures including coarse RCA, even at 100% replacement levels. The use of RCA did not significantly increase the drying shrinkage of concrete. It was shown that the use of RCA significantly reduced the cracking risk of concrete from “high” to “moderate-high” when incorporated into a high cracking risk mixture.

Poor water quality and light reduction owing to anthropogenic impacts are the most widespread causes behind marine submerged angiosperm (seagrass) declines, worldwide. Seagrasses could respond to sustained environmental stresses, such as increasing water depth and light reduction, through morphological changes, particularly shoot density and/or biomass reductions. The seagrass Zostera japonica Asch. and Graebn. has been introduced to the Pacific Coast of North America, but it is widely threatened in its native northwestern Pacific Coast range alongside the east coast of China. The main aims of this study were to determine: 1) the depth limit of Z. japonica growth in its native range, and 2) how light availability affects the growth and recruitment of Z. japonica. To achieve these aims, we investigated the temporal responses of Z. japonica shoots and seeds from an intertidal donor site, Swan Lake, to light availability at water depths ranging from 1 to 6 m using in situ suspended cultures deployed in the experimental site, Ailian Bay, off the coast of Weihai City, China. The results showed that the transplanted Z. japonica shoots and seeds could survive for the duration of their annual growth cycle, permanently underwater, at a depth ≤2 m. There was a significant inverse relationship between water depth and time to complete shoot loss, despite temporally varying water clarity levels. Due to the local turbidity of the waters in Ailian Bay, a depth of 2 m yielded sufficient light deprivation (5%-37% surface irradiance) to negatively affect the seagrass shoot density. Our results suggest that this intertidal species can potentially persist in shallow subtidal areas following transplantation with shoots and seeds. The findings may also serve as useful information for local seagrass distribution limits, and will facilitate their habitat establishment and restoration efforts.

Implementing management actions to achieve environmental outcomes requires defining and quantifying ecological targets, but this is a complex challenge, and there are few examples of how to quantitatively set them in complex dynamic marine ecosystems. Here we develop a methodology to devise ‘desired state’ for tropical seagrasses in Cleveland Bay, northern Australia, in the Great Barrier Reef World Heritage Area. Analysis of diverse species assemblages was used to define seagrass communities as indicators of the region’s ecological value. Multivariate regression trees assigned 8000 observations of species presence/absence and habitat characteristics from 2007 to 2017 into seven community types. Generalized Linear Models were used to assess annual variation in above-ground biomass of each seagrass community. Reference subsets of the data expressing high biomass and spatial extent were identified, and desired state was defined as the mean and 95% confidence intervals. This approach rests on the assumption that seagrass resilience and its ecosystem services are met when the diverse seagrass communities reach desired state. This method required a data set that spanned a range in seagrass conditions, but which may have been compromised by a history of pressures. Our method for defining desired state provides evidence-based targets that can be used within an adaptive management framework that prioritises and implements management actions.

Seagrass ecosystems are increasingly subjected to multiple interacting stressors, making the consequent trajectories difficult to predict. Here, we present a new process-based model of seagrass decline in response to cumulative light and temperature stress. The model is calibrated to laboratory datasets for Great Barrier Reef seagrasses using Bayesian inference. Our model, which is fit to both physiological and morphological data, supports the hypothesis that physiological carbon loss rate controls the shoot density decline rate of seagrasses. The model predicts the time to complete shoot loss, and a new, generalisable, cumulative stress index that indicates the potential seagrass shoot density decline based on the time period of cumulative stress. All model predictions include uncertainty estimates based on uncertainty in the model fit to the data. The calibrated model is packaged into a computer program that can forecast the potential declines of seagrasses due to cumulative light and temperature stress.

Predicting algal population dynamics using models informed by experimental data has been used as a strategy to inform the management and control of harmful cyanobacterial blooms. We selected toxic bloom-forming species Microcystis spp. and Raphidiopsis raciborskii (basionym Cylindrospermopsis raciborskii) for further examination as they dominate in 78 % and 17 %, respectively, of freshwater cyanobacterial blooms (cyanoHABs) reported globally over the past 30 years. Field measurements of cyanoHABs are typically based on biomass accumulation, but laboratory experiments typically measure growth rates, which are an important variable in cyanoHAB models. Our objective was to determine the usefulness of laboratory studies of these cyanoHAB growth rates for simulating the species dominance at a global scale. We synthesized growth responses of M. aeruginosa and R. raciborskii from 20 and 16 culture studies, respectively, to predict growth rates as a function of two environmental variables, light and temperature. Predicted growth rates of R. raciborskii exceeded those of M. aeruginosa at temperatures ≳ 25 °C and light intensities ≳ 150 μmol photons m−2 s-1. Field observations of biomass accumulation, however, show that M. aeruginosa dominates over R. raciborskii, irrespective of climatic zones. The mismatch between biomass accumulation measured in the field, and what is predicted from growth rate measured in the laboratory, hinders effective use of culture studies to predict formation of cyanoHABs in the natural environment. The usefulness of growth rates measured may therefore be limited, and field experiments should instead be designed to examine key physiological attributes such as colony formation, buoyancy regulation and photoadaptation. Improving prediction of cyanoHABs in a changing climate requires a more effective integration of field and laboratory approaches, and an explicit consideration of strain-level variability.

Dopamine transporter (DAT) SPECT imaging is widely used for the diagnosis of Parkinson’s disease (PD). Investigations on quantitative analysis of DAT images have been performed using radiomic features together with non-imaging features to predict a patient’s outcome. The purpose of this study is taking the entire DAT image, without feature extraction, predicting a patient’s motor function indicated by part III of the unified Parkinson’s disease rating scale (UPDRS) using a convolutional neural network (CNN). We cast the motor function score prediction as a categorization problem that decides on whether a given patient will have a score below or above 30 in 4 years from baseline. This prediction practice was first conducted using only the baseline UPDRS_III score as the input, which resulted in an accuracy of 64.5%. The performance was then evaluated using the baseline score with the DAT image to assess whether the latter has added value for prediction. The resulting mean accuracy was significantly improved, reaching to 70.7%. This CNN-based motor function score predicting scheme demonstrates the contribution of DAT SPECT images, which shows their promise in improving progression tracking for patients with PD.

Myocardial perfusion (MP) PET imaging plays an important role in risk assessment and stratification of patients with coronary artery disease. In this work, we developed an anatomy-assisted maximum a posteriori (MAP) reconstruction method incorporating a wavelet-based joint entropy (WJE) prior for MP PET imaging. Using the XCAT phantom, we first simulated three MP PET datasets, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular myocardium. We then simulated MP PET datasets of the three cases with respiratory and cardiac (RC) motion to represent realistic clinical situations. Moreover, two MR image datasets of the same subjects without and with RC motion were simulated without the perfusion defect correspondence. Using the simulated data, the proposed method was evaluated quantitatively in terms of noise-contrast tradeoff, and compared with the post-smoothed maximum-likelihood and the conventional MAP methods. The detectability of perfusion defects with various myocardial coverage was also evaluated through receiver operating characteristic analysis using the channelized Hotelling observer. The results demonstrated that the WJE-MAP method improved the noise-contrast tradeoff, leading to significantly enhanced defect detectability over the other two methods in the non-transmural defects, while maintaining comparable performance in the transmural defect. In addition to the simulation study, the proposed method was further evaluated on the acquired PET/MRI data of a Jaszczak phantom with cold rods. Compared with the other two methods, the WJE-MAP method improved the tradeoff between noise and contrast in the smaller rods, thereby indicating its clinical potential for improving defect detectability in MP PET/MR imaging.